Large Language Model Engineering Map 1. Data Collection and Preparation 1.1 Web Scraping 1.1.1 Crawling websites 1.1.2 Extracting text data 1.1.3 Handling different file formats (HTML, PDF, etc.) 1.2 Corpus Creation 1.2.1 Combining data from various sources 1.2.2 Data cleaning and preprocessing 1.2.3 Tokenization and normalization 1.3 Data Filtering 1.3.1 Removing low-quality or irrelevant data 1.3.2 Handling duplicates and near-duplicates 1.3.3 Balancing data across domains or topics 1.4 Data Augmentation 1.4.1 Back-translation 1.4.2 Synonym replacement 1.4.3 Random insertion, deletion, or swapping 2. Model Architecture Design 2.1 Transformer-based Models 2.1.1 Attention mechanisms 2.1.2 Multi-head attention 2.1.3 Positional encoding 2.2 Encoder-Decoder Models 2.2.1 Encoder architecture 2.2.2 Decoder architecture 2.2.3 Attention mechanisms between encoder and decoder 2.3 Autoregressive Models 2.3.1 Causal language modeling 2.3.2 Next-token prediction 2.3.3 Masked language modeling 2.4 Model Scaling 2.4.1 Increasing model depth (number of layers) 2.4.2 Increasing model width (hidden dimension size) 2.4.3 Balancing depth and width for optimal performance 2.5 Parameter Efficiency Techniques 2.5.1 Weight sharing 2.5.2 Low-rank approximations 2.5.3 Pruning and sparsity 3. Training Strategies 3.1 Pretraining 3.1.1 Unsupervised pretraining on large corpora 3.1.2 Masked language modeling objectives 3.1.3 Next sentence prediction objectives 3.2 Fine-tuning 3.2.1 Adapting pretrained models to specific tasks 3.2.2 Transfer learning techniques 3.2.3 Few-shot and zero-shot learning 3.3 Optimization Algorithms 3.3.1 Stochastic Gradient Descent (SGD) 3.3.2 Adam and its variants (AdamW, etc.) 3.3.3 Learning rate scheduling 3.4 Regularization Techniques 3.4.1 Dropout 3.4.2 Weight decay 3.4.3 Early stopping 3.5 Distributed Training 3.5.1 Data parallelism 3.5.2 Model parallelism 3.5.3 Pipeline parallelism 4. Evaluation and Testing 4.1 Perplexity Metrics 4.1.1 Cross-entropy loss 4.1.2 Bits per character (BPC) 4.1.3 Perplexity per word (PPL) 4.2 Downstream Task Evaluation 4.2.1 Language understanding tasks (GLUE, SuperGLUE) 4.2.2 Question answering tasks (SQuAD, TriviaQA) 4.2.3 Language generation tasks (summarization, translation) 4.3 Human Evaluation 4.3.1 Fluency and coherence 4.3.2 Relevance and informativeness 4.3.3 Diversity and creativity 4.4 Bias and Fairness Assessment 4.4.1 Identifying and measuring biases 4.4.2 Debiasing techniques 4.4.3 Fairness evaluation metrics 5. Deployment and Inference 5.1 Model Compression 5.1.1 Quantization 5.1.2 Pruning 5.1.3 Knowledge distillation 5.2 Inference Optimization 5.2.1 Efficient attention mechanisms 5.2.2 Caching and reuse of intermediate results 5.2.3 Hardware-specific optimizations (GPU, TPU) 5.3 Serving Infrastructure 5.3.1 REST APIs 5.3.2 Containerization (Docker) 5.3.3 Scalability and load balancing 5.4 Monitoring and Maintenance 5.4.1 Performance monitoring 5.4.2 Error logging and alerting 5.4.3 Model versioning and updates 6. Ethical Considerations 6.1 Privacy and Data Protection 6.1.1 Anonymization and pseudonymization 6.1.2 Secure data storage and access control 6.1.3 Compliance with regulations (GDPR, CCPA) 6.2 Bias and Fairness 6.2.1 Identifying sources of bias 6.2.2 Mitigating biases in data and models 6.2.3 Ensuring fair and unbiased outputs 6.3 Transparency and Explainability 6.3.1 Model interpretability techniques 6.3.2 Providing explanations for model decisions 6.3.3 Communicating limitations and uncertainties 6.4 Responsible Use and Deployment 6.4.1 Preventing misuse and malicious applications 6.4.2 Establishing guidelines and best practices 6.4.3 Engaging with stakeholders and the public 7. Future Directions and Research 7.1 Multimodal Models 7.1.1 Integrating text, images, and audio 7.1.2 Cross-modal reasoning and generation 7.1.3 Applications in robotics and embodied AI 7.2 Lifelong Learning and Adaptation 7.2.1 Continual learning without catastrophic forgetting 7.2.2 Online learning and adaptation to new data 7.2.3 Transfer learning across tasks and domains 7.3 Reasoning and Knowledge Integration 7.3.1 Incorporating structured knowledge bases 7.3.2 Combining symbolic and sub-symbolic approaches 7.3.3 Enabling complex reasoning and inference 7.4 Efficient and Sustainable AI 7.4.1 Reducing computational costs and carbon footprint 7.4.2 Developing energy-efficient hardware and algorithms 7.4.3 Promoting sustainable practices in AI research and deployment 8. Model Interpretability and Analysis 8.1 Attention Visualization 8.1.1 Visualizing attention weights and patterns 8.1.2 Identifying important input tokens and dependencies 8.1.3 Analyzing attention across layers and heads 8.2 Probing and Diagnostic Classifiers 8.2.1 Evaluating model's understanding of linguistic properties 8.2.2 Assessing model's ability to capture syntactic and semantic information 8.2.3 Identifying strengths and weaknesses of the model 8.3 Counterfactual Analysis 8.3.1 Generating counterfactual examples 8.3.2 Analyzing model's sensitivity to input perturbations 8.3.3 Identifying biases and spurious correlations 9. Domain Adaptation and Transfer Learning 9.1 Unsupervised Domain Adaptation 9.1.1 Aligning feature spaces across domains 9.1.2 Adversarial training for domain-invariant representations 9.1.3 Self-training and pseudo-labeling techniques 9.2 Few-Shot Domain Adaptation 9.2.1 Meta-learning approaches 9.2.2 Prototypical networks and metric learning 9.2.3 Adapting models with limited labeled data from target domain 9.3 Cross-Lingual Transfer Learning 9.3.1 Multilingual pretraining 9.3.2 Zero-shot cross-lingual transfer 9.3.3 Adapting models to low-resource languages 10. Model Compression and Efficiency 10.1 Knowledge Distillation 10.1.1 Teacher-student framework 10.1.2 Transferring knowledge from large to small models 10.1.3 Distilling attention and hidden states 10.2 Quantization and Pruning 10.2.1 Reducing model size through lower-precision representations 10.2.2 Pruning less important weights and connections 10.2.3 Balancing compression and performance trade-offs 10.3 Neural Architecture Search 10.3.1 Automating the design of efficient model architectures 10.3.2 Searching for optimal hyperparameters and layer configurations 10.3.3 Multi-objective optimization for performance and efficiency 11. Robustness and Adversarial Attacks 11.1 Adversarial Examples 11.1.1 Generating input perturbations to fool models 11.1.2 Evaluating model's sensitivity to adversarial attacks 11.1.3 Developing defenses against adversarial examples 11.2 Out-of-Distribution Detection 11.2.1 Identifying inputs that are different from training data 11.2.2 Calibrating model's uncertainty estimates 11.2.3 Rejecting or flagging out-of-distribution examples 11.3 Robust Training Techniques 11.3.1 Adversarial training with perturbed inputs 11.3.2 Regularization methods for improved robustness 11.3.3 Ensemble methods and model averaging 12. Multilingual and Cross-Lingual Models 12.1 Multilingual Pretraining 12.1.1 Training models on data from multiple languages 12.1.2 Leveraging cross-lingual similarities and transfer 12.1.3 Handling language-specific characteristics and scripts 12.2 Cross-Lingual Alignment 12.2.1 Aligning word embeddings across languages 12.2.2 Unsupervised cross-lingual mapping 12.2.3 Parallel corpus mining and filtering 12.3 Zero-Shot Cross-Lingual Transfer 12.3.1 Transferring knowledge from high-resource to low-resource languages 12.3.2 Adapting models without labeled data in target language 12.3.3 Evaluating cross-lingual generalization and performance 13. Dialogue and Conversational AI 13.1 Dialogue State Tracking 13.1.1 Representing and updating dialogue context 13.1.2 Handling multiple domains and intents 13.1.3 Incorporating external knowledge and memory 13.2 Response Generation 13.2.1 Generating coherent and relevant responses 13.2.2 Incorporating personality and emotion 13.2.3 Handling multi-turn conversations and context 13.3 Dialogue Evaluation Metrics 13.3.1 Automatic metrics for response quality and coherence 13.3.2 Human evaluation of dialogue systems 13.3.3 Assessing engagement, empathy, and user satisfaction 14. Commonsense Reasoning and Knowledge Integration 14.1 Knowledge Graphs and Ontologies 14.1.1 Representing and storing structured knowledge 14.1.2 Integrating knowledge graphs with language models 14.1.3 Reasoning over multiple hops and relations 14.2 Commonsense Knowledge Bases 14.2.1 Collecting and curating commonsense knowledge 14.2.2 Incorporating commonsense reasoning into language models 14.2.3 Evaluating models' commonsense understanding and generation 14.3 Knowledge-Grounded Language Generation 14.3.1 Generating text grounded in external knowledge sources 14.3.2 Retrieving relevant knowledge for context-aware generation 14.3.3 Ensuring factual accuracy and consistency 15. Few-Shot and Zero-Shot Learning 15.1 Meta-Learning Approaches 15.1.1 Learning to learn from few examples 15.1.2 Adapting models to new tasks with limited data 15.1.3 Optimization-based and metric-based meta-learning 15.2 Prompt Engineering and In-Context Learning 15.2.1 Designing effective prompts for few-shot learning 15.2.2 Leveraging language models' in-context learning capabilities 15.2.3 Exploring prompt variations and task-specific adaptations 15.3 Zero-Shot Task Generalization 15.3.1 Transferring knowledge to unseen tasks without fine-tuning 15.3.2 Leveraging task descriptions and instructions 15.3.3 Evaluating models' ability to generalize to novel tasks